class_docstr: Multimodal Combination module.
code: def __init__(
      self,
      n_heads,
      n_layers,
      hidden_dim,
      ffn_size,
      reduction=True,
      attention_dropout=0.0,
      relu_dropout=0.0,
      learn_positional_embeddings=False,
  ):
      super().__init__()
      self.ffn_size = ffn_size
      self.n_layers = n_layers
      self.n_heads = n_heads
      self.out_dim = hidden_dim
      self.dim = hidden_dim
      self.reduction = reduction
      assert (
          hidden_dim % n_heads == 0
      ), "MM-Combiner dim must be multiple of n_heads"
      n_positions = 1024
      self.position_embeddings = nn.Embedding(n_positions, hidden_dim)
      if not learn_positional_embeddings:
          create_position_codes(
              n_positions,
              hidden_dim,
              out=self.position_embeddings.weight,
          )
      else:
          nn.init.normal_(
              self.position_embeddings.weight, 0, hidden_dim**-0.5
          )
  
      self.layers = nn.ModuleList()
      for _ in range(self.n_layers):
          self.layers.append(
              TransformerEncoderLayer(
                  n_heads,
                  hidden_dim,
                  ffn_size,
                  attention_dropout,
                  relu_dropout,
              )
          )
  
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  import torch
  from torch import nn
  from parlai.utils.io import PathManager
  from parlai.agents.transformer.modules import TransformerEncoder, create_position_codes, TransformerEncoderLayer
  from projects.personality_captions.transresnet.modules import TransresnetModel, load_fasttext_embeddings
label_desc: Use image node_id 1 to create a new MultimodalCombiner object from inherited base classes: nn with example: obj = MultimodalCombiner(n_heads, n_layers, hidden_dim, ffn_size, reduction, attention_dropout, relu_dropout, learn_positional_embeddings)